Metadata-Version: 1.0
Name: inspyred
Version: 1.0
Summary: A framework for creating bio-inspired computational intelligence algorithms in Python.
Home-page: http://inspyred.github.com
Author: Aaron Garrett
Author-email: aaron.lee.garrett@gmail.com
License: GPLv3+
Download-URL: https://github.com/inspyred/inspyred/downloads/inspyred-1.0.tar.gz
Description: ``inspyred`` -- A framework for creating bio-inspired computational intelligence algorithms in Python.
        ------------------------------------------------------------------------------------------------------
        
        inspyred is a free, open source framework for creating biologically-inspired 
        computational intelligence algorithms in Python, including evolutionary 
        computation, swarm intelligence, and immunocomputing. Additionally, inspyred 
        provides easy-to-use canonical versions of many bio-inspired algorithms for 
        users who don't need much customization.
        
        
        Example
        =======
        
        The following example illustrates the basics of the inspyred package. In this 
        example, candidate solutions are 10-bit binary strings whose decimal values 
        should be maximized::
        
           import random 
           import time 
           import inspyred
        
           def generate_binary(random, args):
               bits = args.get('num_bits', 8)
               return [random.choice([0, 1]) for i in range(bits)]
        
           @inspyred.ec.evaluators.evaluator
           def evaluate_binary(candidate, args):
               return int("".join([str(c) for c in candidate]), 2)
        
           rand = random.Random()
           rand.seed(int(time.time()))
           ga = inspyred.ec.GA(rand)
           ga.observer = inspyred.ec.observers.stats_observer
           ga.terminator = inspyred.ec.terminators.evaluation_termination
           final_pop = ga.evolve(evaluator=evaluate_binary,
                                 generator=generate_binary,
                                 max_evaluations=1000,
                                 num_elites=1,
                                 pop_size=100,
                                 num_bits=10)
           final_pop.sort(reverse=True)
           for ind in final_pop:
               print(str(ind))
        
        
        Requirements
        ============
        
          * Requires at least Python 2.6+ or 3+.
          * Numpy and Pylab are required for several functions in ``ec.observers``.
          * Pylab and Matplotlib are required for several functions in ``ec.analysis``.
          * Parallel Python (pp) is required if ``ec.evaluators.parallel_evaluation_pp`` is used.
        
        
        License
        =======
        
        This package is distributed under the GNU General Public License 
        version 3.0 (GPLv3). This license can be found online at
        http://www.opensource.org/licenses/gpl-3.0.html.
          
        
        Resources
        =========
        
          * Homepage: http://inspyred.github.com
          * Email: aaron.lee.garrett@gmail.com
        
Keywords: python,optimization,evolutionary computation,genetic algorithm,particle swarm,estimation of distribution,differential evolution,nsga,paes,island model,multiobjective,ant colony
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
